Towards accelerated, experimental-theoretical discovery of novel porous liquids Austin Mroz, Rebecca L. Greenaway, Kim E. Jelfs Imperial College London, UK Improved gas separation technologies, specifically those for carbon capture and sequestration (CCS), are necessary to combat the effects of climate change. [1,2] Current industrial CCS methods are highly toxic, [3] or possess non-uniform pore channels, [4] which limit CO2 separation. Porous liquids (PLs) offer the permanent porosity of nanoporous solid-state materials, while maintaining the fluidity and fast mass transfer capabilities of liquids – making them ideal CCS technologies. [5,6] Their simple composition (size-excluded solvent paired with a porous motif) provides a diverse chemical space for design initiatives. Optimal generation of novel PLs for targeted applications rests on the adequate selection of these components. Thus, the ideal porous motif/size- excluded liquid pair must be selected for optimal gas uptake under target processing conditions; this is well-suited to predictive and generative powers of machine learning (ML). Yet, the youth of the PL field limits the availability of data necessary to train these models. This work leverages ML-driven property prediction capabilities with detailed electronic structure and thermodynamic properties obtained from quantum mechanical and classical simulations to identify novel, viable PLs ideal for CCS applications. We provide and validate the computational contribution towards a theoretical-experimental workflow for the discovery of high-performing PLs for CCS applications, and output novel modelling and predictive techniques for handling mixtures. This allows us to screen millions of possible PLs using information from <10,000 chemical simulations; we validate our approach experimentally by synthesizing and measuring high-performing candidate PLs. The resulting workflow offers an economic, accelerated solution towards novel PL discovery. References 1. Ozdemir, J, et. al., Front. Energy Res., 2019, 7, 77 2. Omodolor, I. S., et. al., Ind. Eng. Chem. Res., 2020, 59, 17612-17631
3. Lu, X., et al., J. Mater. Chem. A, 2015, 3, 12118-12132 4. Xiong, J., et al., ChemCatChem., 2017, 9, 2584-2587 5. O’Reilly, N., et al., Chem. Eur. J., 2007, 13, 3020-3025 6. Shan, W., et al., ACS Appl Mater. Interfaces, 2018, 10, 32-36
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